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All Journal Jurnal Informatika International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science CYBERNETICS
Dewi Pramudi Ismi
Universitas Ahmad Dahlan
Computer Science & IT Engineering

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Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU Adhi Prahara; Dewi Pramudi Ismi; Ahmad Azhari
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p40-49

Abstract

K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when dealing with outliers and arbitrary distance metric also in the situation when the mean or median does not exist within data. However, k-medoids suffers a high computational complexity. Partitioning Around Medoids (PAM) has been developed to improve k-medoids clustering, consists of build and swap steps and uses the entire dataset to find the best potential medoids. Thus, PAM produces better medoids than other algorithms. This research proposes the parallelization of PAM in k-medoids clustering on GPU to reduce computational time at the swap step of PAM. The parallelization scheme utilizes shared memory, reduction algorithm, and optimization of the thread block configuration to maximize the occupancy. Based on the experiment result, the proposed parallelized PAM k-medoids is faster than CPU and Matlab implementation and efficient for large dataset.
K-means clustering based filter feature selection on high dimensional data Dewi Pramudi Ismi; Shireen Panchoo; Murinto Murinto
International Journal of Advances in Intelligent Informatics Vol 2, No 1 (2016): March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i1.54

Abstract

With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload. Therefore the aim of this paper is to use feature selection to decrease the computation load by reducing the size of high dimensional data. Selecting subsets of features which represent all features were used. Hence the process is two-fold; discarding irrelevant data and choosing one feature that representing a number of redundant features. There have been many studies regarding feature selection, for example backward feature selection and forward feature selection. In this study, a k-means clustering based feature selection is proposed. It is assumed that redundant features are located in the same cluster, whereas irrelevant features do not belong to any clusters. In this research, two different high dimensional datasets are used: 1) the Human Activity Recognition Using Smartphones (HAR) Dataset, containing 7352 data points each of 561 features and 2) the National Classification of Economic Activities Dataset, which contains 1080 data points each of 857 features. Both datasets provide class label information of each data point. Our experiment shows that k-means clustering based feature selection can be performed to produce subset of features. The latter returns more than 80% accuracy of classification result.
Bottom-up visual attention model for still image: a preliminary study Adhi Prahara; Murinto Murinto; Dewi Pramudi Ismi
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.469

Abstract

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.
Clustering based feature selection using Partitioning Around Medoids (PAM) Dewi Pramudi Ismi; Murinto Murinto
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17620

Abstract

High-dimensional data contains a large number of features. With many features, high dimensional data requires immense computational resources, including space and time. Several studies indicate that not all features of high dimensional data are relevant to classification result. Dimensionality reduction is inevitable and is required due to classifier performance improvement. Several dimensionality reduction techniques were carried out, including feature selection techniques and feature extraction techniques. Sequential forward feature selection and backward feature selection are feature selection using the greedy approach. The heuristics approach is also applied in feature selection, using the Genetic Algorithm, PSO, and Forest Optimization Algorithm. PCA is the most well-known feature extraction method. Besides, other methods such as multidimensional scaling and linear discriminant analysis. In this work, a different approach is applied to perform feature selection. Cluster analysis based feature selection using Partitioning Around Medoids (PAM) clustering is carried out. Our experiment results showed that classification accuracy gained when using feature vectors' medoids to represent the original dataset is high, above 80%.
Peringkasan Ekstraktif Teks Bahasa Indonesia dengan Pendekatan Unsupervised Menggunakan Metode Clustering Dewi Pramudi Ismi; Fahri Ardianto
CYBERNETICS Vol 3, No 02 (2019): CYBERNETICS
Publisher : Universitas Muhammadiyah Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29406/cbn.v3i02.2290

Abstract

Perkembangan teknologi informasi yang pesat membuat volume informasi yang dapat diakses oleh manusia melalui internet menjadi tidak terbendung. Hal ini menyebabkan pembaca harus dapat memilah informasi yang penting maupun merangkum informasi yang sangat masif. Pembaca perlu merangkum/meringkas banyaknya informasi menjadi informasi utama yang layak untuk ditelaah. Proses merangkum/meringkas informasi yang banyak dari berbagai sumber merupakan permasalahan yang tidak mudah. Peringkasan teks dapat dilakukan secara otomatis oleh komputer menggunakan teknologi kecerdasan buatan. Penelitian ini mengusulkan metode peringkasan teks berbasis clustering dengan K-means clustering. Metode yang diusulkan telah diuji dengan Rouge score menggunakan dataset benchmark. Berdasarkan hasil pengujian, sistem peringkasan teks yang dibangun cukup baik, yaitu memiliki nilai F1 score dari Rouge-1 = 49,37%, Rouge-2 = 38,18% dan Rouge-L = 46,87%. Hasil yang diperoleh adalah bahwa metode ini lebih baik dari 3 metode unsupervised yang telah digunakan sebelumnya yaitu SumBasic, LSA dan LexRank.
Co-Authors Adhi Prahara Adhi Prahara Azhari, Ahmad Fahri Ardianto Murinto Murinto Shireen Panchoo